Formulations and solution algorithms for dynamic assembly routing problem

We study a dynamic version of the assembly routing problem. The assembly lot sizing section deals with decisions concerning the production phase, whereas the routing section organises the collection of raw materials necessary for the production. Traditionally, these two problems are treated separately, and more specifically, in a hierarchical way. We propose three linear programming models: a non-vehicle index model, a two-commodity flow formulation and a logic-based benders decomposition. We develop aggregated rounded capacity constraints for non-vehicle index model and separated them dynamically during the Branch & Cut procedure. Logic-based benders decomposition algorithm solves the Dynamic Assembly Routing Problem iteratively and obtains a feasible solution at each iteration. The numerical tests show that, the two first models are particularly effective at finding the optimal solutions in a reasonable amount of time on instances with up to 50 components and 3 periods.     

The Use of a Eutectic Mixture of Olive Pomace Oil Fatty Amides to Easily Prepare Sulfated Amides Applied as Lime Soap Dispersants

Sulfated diethanolamides of fatty acids are known to be effective lime soap dispersing agents. However, their preparation from fatty acids via fatty amides requires the use of organic solvents due to the high viscosity of both fatty amides and sulfated fatty amides. This study shows that the preparation of sulfated fatty amides is relatively easy when using olive pomace oil as the raw material. The latter, is converted into sulfated fatty amides by performing the following steps: saponification, hydrolysis, esterification, amidation, and sulfation. In the final step, the mixture obtained has sufficient fluidity, due to its high linoleic acid amide content, to obviate the use of organic solvents, as usually suggested in the literature. Characterization of the product was carried out by chemical analyses, FTIR, ¹³C NMR, GC, and HPLC. It was shown that the yield of the amidation reaction is about 80%, and that of the sulfation reaction can exceed 100% against the pure amide (more than one sulfate group could be linked to one amide molecule). On the other hand, the Borghetty test showed that the product is an effective dispersant with a lime soap dispersing power equal to five.     

Impedimetric microbial biosensor based on single wall carbon nanotube modified microelectrodes for trichloroethylene detection

Contamination of soils and groundwaters with persistent organic pollutants is a matter of increasing concern. The most common organic pollutants are chlorinated hydrocarbons such as perchloroethylene and trichloroethylene (TCE). In this study, we developed a bacterial impedimetric biosensor for TCE detection, based on the immobilization of Pseudomonas putida F1 strain on gold microelectrodes functionalized with single wall carbon nanotubes covalently linked to anti-Pseudomonas antibodies. The different steps of microelectrodes functionalization were characterized by electrochemical impedance and atomic force spectroscopies, and analytical performances of the developed microbial biosensor were determined. The impedimetric biosensor response was linear with TCE concentration up to 150 μg L⁻¹ and a low limit of detection (20 μg L⁻¹) was achieved. No significant loss of signal was observed after 4 weeks of storage at 4 °C in phosphate buffer saline pH 7 (three to four measurements a week). After 5 weeks, 90% of the initial value still remained. cis-1,2-Dichloroethylene and vinylchloride, the main TCE degradation products, did not significantly interfere with TCE. The microbial sensor was finally applied to the determination of TCE in natural water samples spiked at the 30, 50 and 75 μg L⁻¹ levels. Recoveries were very good, ranging from 100 to 103%.     

Genetic algorithm for supply planning in two-level assembly systems with random lead times

This paper examines supply planning for two-level assembly systems under lead time uncertainties. It is supposed that the demand for the finished product and its due date are known. The assembly process at each level begins when all necessary components are in inventory. If the demand for the finished product is not delivered at the due date, a tardiness cost is incurred. In the same manner, a holding cost at each level appears if some components needed to assemble the same semi-finished product arrive before beginning the assembly at this level. It is assumed also that the lead time at each level is a random discrete variable. The expected cost is composed of the tardiness cost for finished product and the holding costs of components at levels 1 and 2. The objective is to find the release dates for the components at level 2 in order to minimize the total expected cost. For this new problem, a genetic algorithm is suggested. The proposed algorithm is evaluated with a variety of supply chain settings in order to verify its robustness across different supply chain scenarios. Moreover, the effect of a local search on the performance of the Genetic Algorithm in terms of solution quality, convergence and computation time is also investigated.     

Multi-objective optimization for inventory control in two-level assembly systems under uncertainty of lead times

Supply planning for two-level assembly systems under lead time uncertainties is considered. It is supposed that the demand for the finished product and its due date are known. The assembly process at each level begins when all necessary components are in inventory. A holding cost at each level appears if some components needed to assemble the same semi-finished product arrive before beginning the assembly at this level. It is assumed also that the component lead time is a random discrete variable. The objective is to find the release dates for the components at level 2 in order to minimize the expected component holding costs and to maximize the customer service level for the finished product. For this new problem, we consider two multi-objective approaches, which are both based on genetic algorithms. They are evaluated with a variety of supply chain settings, and their respective performance is reported and commented. These two heuristics permitted to obtain interesting results within a reasonable computational time.     

A new HRP/catalase biosensor based on microconductometric transduction for nitrite determination

An interdigitated conductimetric electrode system using a combination of peroxidase/catalase has been developed to determine nitrite in water samples. A peroxidase (HRP) was located in the inner layer while the outer contained catalase. Catalase catalyzed the breakdown of H₂O₂ into H₂O and O₂ thus consuming totally H₂O₂, the substrate of HRP. The latter, in presence of H₂O₂, generates a conductometric signal due to the reduction of H₂O₂. Nitrite was selected as an inhibitor of catalase. In the presence of H₂O₂, the nitrite addition blocked the H₂O₂ consumption by catalase. Since nitrite had no effect on HRP activity, its inhibitive effect on catalase leads to an increase in the conductometric signal. The bienzyme sensor exhibits an increase in conductometric response for nitrite concentration, leading to high values of conductivity. In both case, the detection limit of nitrite is 0.3 µM and for bienzyme sensor the dynamic range is from 0.3 µM to 446 µM.     

Minimising the makespan in the two-machine job shop problem under availability constraints

Classical scheduling problem assumes that machines are available during the scheduling horizon. This assumption may be justified in some situations but it does not apply if maintenance requirements, machine breakdowns or other availability constraints have to be considered. In this paper, we treat a two-machine job shop scheduling problem with one availability constraint on each machine to minimise the maximum completion time (makespan). The unavailability periods are known in advance and the processing of an operation cannot be interrupted by an unavailability period (non-preemptive case). We present in our approach properties dealing with permutation dominance and the optimality of Jackson's rule under availability constraints. In order to evaluate the effectiveness of the proposed approach, we develop two mixed integer linear programming models and two schemes for a branch and bound method to solve the tackled problem. Computational results validate the proposed approach and prove the efficiency of the developed methods.